Can computers think? Can they use reason to develop their concepts, solve complex problems, play games, understand our languages? This can be possible through artificial intelligence. Artificial intelligence is the study of how computers can be made to act intelligently. AI is the simulation of human intelligence processes by machines, especially computer systems.
These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. A typical AI analyzes its environment and takes actions that maximize its chance of success. Reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects are the basic goals of AI research.
History of Artificial Intelligence
The concept of the AI was introduced in the early days. During the Second World War, noted British computer scientist Alan Turing worked to crack the ‘Enigma’ code which was used by German forces to send messages securely. He also presented his idea in the model of the Turing machine, which is still popular in computer science.
Some years after the end of World War 2, Turing introduced his widely known Turing Test, which was an attempt to define machines intelligent. In 1956, American computer scientist John McCarthy organized the Dartmouth Conference, at which the term ‘Artificial Intelligence’ was first adopted. Researchers in the 1960s emphasized the development of algorithms to fix mathematical issues and geometrical theories.
In the late 1960s, computer scientists worked on Machine Vision Learning and developing machine learning in robots. The first’ smart’ humanoid robot, WABOT-1, was constructed in 1972 in Japan. Because of the over-optimistic settings and the not occurred breakthroughs U.S. and British government cut off exploratory research in AI.
The following years were called (first) AI Winter. The interest of publicity on Artificial Intelligence decreased. This was around 1974. In the late 1990s, American corporations once again became interested in AI. In 1997, IBM’s Deep Blue defeated became the first computer to beat a reigning world chess champion, Garry Kasparov. Some AI funding dried up when the dotcom bubble burst in the early 2000s.
Yet machine learning continued its march, largely thanks to improvements in computer hardware. In the past 15 years, Amazon, Google, Baidu, and others leveraged machine learning to their huge commercial advantage. Other than processing user data to understand consumer behavior, these companies have continued to work on computer vision, natural language processing, and a whole host of other AI applications.
The ubiquity of AI may now seem like it’s not far from achieving intelligence at the human level. But AI requires huge quantities of information to learn from a single experience, unlike our brains.
Different Types of Artificial Intelligence
AI can be classified in many ways. There are mainly two ways of classification.
Type 1 (Based on the capabilities)
- Weak AI or Narrow AI: Narrow AI is the most common form of AI in the world of AI. It is a type of AI that can perform a dedicated task with intelligence. Narrow AI is unable to conduct beyond its field or constraints, as it is trained only for one specific task. It is therefore also referred to as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits. Some example of the narrow AI is Apple’s Siri, purchasing suggestions on e-commerce site, self-driving cars, speech recognition, and image recognition.
- Strong AI: Strong AI is the type of AI which could perform any intellectual task with efficiency like a human. The idea behind this AI is to make a system smarter and think like a human on its own. Currently, there is no such general AI system. As systems with general AI are still under research, and it will take lots of effort and time to develop such systems. Some key characteristics of strong AI include capability include the ability to think, to reason, solve the puzzle, make judgments, plan, learn, and communicate on its own.
Type 2 (Based on functionality)
- Reactive machines: these are the most basic type of AI. Such AI systems don’t store any memories or experiences. These machines only focus on current scenarios and react on it as per possible best action. IBM’s Deep Blue system and Google’s AlphaGo are examples of the reactive machines.
- Limited memory: this type of AI can store memories for a limited period. Self-driving cars are one of the best examples of Limited Memory systems. These cars can store the recent speed of nearby cars, the distance of other cars, speed limits, and other information to navigate the road.
- Theory of mind: Theory of Mind AI should understand human emotions, people, beliefs, and be able to interact socially like humans. This type of AI machine is still not developed, but researchers are making lots of effort and improvement for developing such AI machines.
- Self-awareness: Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent and will have their consciousness, sentiments, and self-awareness. Self-Awareness AI does not exist in reality still and it is a hypothetical concept.
The Applications of Artificial Intelligence
An AI can be used in various fields where it is necessary to optimize the information and its search, improve the quality of products and the production process. Artificial intelligence technologies do not imitate the person himself, but the behavior of biological neural networks and evolutionary processes in nature. It can be used in medicine to diagnose the diseases and know more about their treatment.
AI can also be used in business for the creation of personal recommendations, increasing the efficiency of shops, optimization of technical support work, optimization of production in the industry. There are many areas where AI can apply and a lot of developments are underway.
AI can be used in various fields. Some of the fields of AI are machine learning, natural language processing, expert systems, vision, speech, planning, and robotics.
Limitations of Artificial Intelligence
AI has many limitations though it is useful in many areas. One of the main limitations is it can fail. Putting a lot of complex tasks on AI, we shouldn’t forget machines can fail. A small error in the calculation can bring a large number of problems. It can also cause data loss.
Confrontation is also another problem in AI. Continuous improvement of logical processes can isolate artificial intelligence from humanity. The aggressive impact of even one state in its interests can cause dangerous and unpredictable consequences.
Replacement can also be another limitation. As artificial intelligence begins to replace a person in various spheres, more and more people will remain unemployed.